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Understanding Receptive Field Models and Neural Coding: Insights from Tuning Curves

This module explores receptive field models and their role in neural coding. Key concepts include tuning curves, the Linear-Nonlinear (LN) model, and the Spike-Triggered Covariance model. We examine how these models provide insights into neuronal function and spatial mapping, with applications in higher brain areas and human epilepsy studies. By measuring spiking responses to various stimuli, we can evaluate the adequacy of tuning curves and predict neuronal responses to complex and dynamic stimuli.

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Understanding Receptive Field Models and Neural Coding: Insights from Tuning Curves

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  1. NEU 501:From Molecules to Systems Module 6: Neural Coding Class 2: Receptive Field Models Michael J Berry II Monday, Dec. 2, 2013

  2. Day 2: Receptive Field Models • Tuning Curves • The Linear-Nonlinear (LN) Model • The Spike-Triggered Covariance Model

  3. Receptive Fields and Tuning Curves • tuning curve: r = f(s)

  4. Receptive Fields and Tuning Curves II • tuning curve: r = f(s)

  5. Higher brain areas represent increasingly complex features • human epilepsy patients: awake, behaving • medial temporal lobe • present different pictures, measure spiking response

  6. Common Stimulus

  7. Another Cell

  8. Another Cell: Stimuli

  9. Common Stimuli

  10. Inadequacy of Tuning Curves • Provides insight into neuronal function, spatial maps • What if there is a stimulus outside of the test set? –want a model that can predict response to an arbitrary stimulus • What about the dynamics of the response? • What about spike times? • What about a stimulus with dynamics?

  11. Day 2: Receptive Field Models • Tuning Curves • The Linear-Nonlinear (LN) Model • The Spike-Triggered Covariance Model

  12. Neural Coding

  13. LNP Model of Neural Representation • Allows time-varying stimulus • Predicts time-varying firing rate • Converts time-varying firing rate into a spike train

  14. Linear Response: Example

  15. Linear Response: Example cont.

  16. Linear Response: Example cont.

  17. LN Model: Applying the Nonlinear Function

  18. Key Property of the LN Model • If the stimulus is white noise… then the linear filter is the spike-triggered stimulus average: • Arises from symmetry in the stimulus ensemble (Chichilnisky) • Implies that you measure all these parameters from data

  19. Measuring the Receptive Field • Reverse correlation to a flickering checkerboard

  20. The Receptive Field •Example from a retinal ganglion cell Temporal Profile Spatial Profile

  21. Finding the Nonlinear Function • Convolve the linear filter with the stimulus: • Find the distribution of effective stimulus values at spike times: • Invert using Bayes’ Rule:

  22. The LN Model 1) Find the spike-triggered stimulus average (STA): 2) Linear filter must be time-reversed STA: 3) Find the effective stimulus, s1(t): 4) Sample s1(t) at the times of spikes: 5) Use Bayes’ Rule to find the nonlinear function:

  23. The END

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